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向精准医学迈进,预测转移性乳腺癌患者的药物敏感性。

Moving toward precision medicine to predict drug sensitivity in patients with metastatic breast cancer.

机构信息

INSERM Unit U981, Gustave Roussy Cancer Campus, Villejuif, France; Department of Surgery, Oncology and Gastroenterology, University of Padova, Padova, Italy.

INSERM Unit U981, Gustave Roussy Cancer Campus, Villejuif, France; Department of Medical Oncology, Gustave Roussy, Villejuif.

出版信息

ESMO Open. 2024 Mar;9(3):102247. doi: 10.1016/j.esmoop.2024.102247. Epub 2024 Feb 23.

Abstract

Tumor heterogeneity represents a major challenge in breast cancer, being associated with disease progression and treatment resistance. Precision medicine has been extensively applied to dissect tumor heterogeneity and, through a deeper molecular understanding of the disease, to personalize therapeutic strategies. In the last years, technological advances have widely improved the understanding of breast cancer biology and several trials have been developed to translate these new insights into clinical practice, with the ultimate aim of improving patients' outcomes. In the era of molecular oncology, genomics analyses and other methodologies are shaping a new treatment algorithm in breast cancer care. In this manuscript, we review the main steps of precision medicine to predict drug sensitivity in breast cancer from a translational point of view. Genomic developments and their clinical implications are discussed, along with technological advancements that could broaden precision medicine applications. Current achievements are put into perspective to provide an overview of the state-of-art of breast cancer precision oncology as well as to identify future research directions.

摘要

肿瘤异质性是乳腺癌的主要挑战之一,与疾病进展和治疗耐药性相关。精准医学已广泛应用于剖析肿瘤异质性,并通过更深入地了解疾病的分子机制,实现治疗策略的个体化。近年来,技术进步极大地提高了我们对乳腺癌生物学的认识,并且已经开展了多项临床试验,以期将这些新的见解转化为临床实践,最终改善患者的预后。在分子肿瘤学时代,基因组学分析和其他方法正在为乳腺癌治疗制定新的治疗算法。在本文中,我们从转化医学的角度综述了精准医学在预测乳腺癌药物敏感性方面的主要步骤。讨论了基因组学的发展及其临床意义,以及可能拓宽精准医学应用的技术进步。本文还将当前的研究成果置于适当的背景下,概述乳腺癌精准肿瘤学的最新进展,并确定未来的研究方向。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/522f/10982863/eaf1a0e82439/gr1.jpg

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